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• Deep Learning Foundations with Pytorch and Tensorflow
  • History and evolution of AI; Why Deep Learning has taken off in recent years
  • History of Neural Natural Language Processing
  • Structure of Artificial Neural Networks
  • Steps in Training an Artificial Neural Network; Parameters and Hyperparameters
• Types of Neural Networks
  • Multi Layer Perceptrons, Recurrent Neural Networks(RNN), LSTM(Long Short Memory Networks)
  • Limitations of LSTM and RNN in modeling long range sequences
• Types of Generative AI Models
  • Generative Adversarial Networks(GANs); Variational Autoencoders; Transformers
  • Transformers and Attention mechanism in detail
  • Generative AI and Large Language Models (Language Models and Foundation Models; Development of Neural Natural Language Processing)
  • Two most popular Generative AI chatbots - BERT and ChatGPT- LLMs that Power these Chatbots-Architecture; Training Data
• NLP with Transformers
  • Generative vs Discriminative models
  • Generative AI Architecture
  • Transforming text to numerical (vector) representations-Word Embeddings (Applications of Word Embeddings); Word Embedding Models
  • Training an LLM
  • Pretraining and Transfer Learning
  • RAG applications
• ChatGPT APIs and Prompt Engineering
  • Prompt Engineering- Zero Shot vs Few Shot Learning
  • Limitations of LLMs; How to overcome with limitations of LLMs with Retrieval Augmented Generation(RAG) with LangChain; Fine-tuning
  • Generative AI Creativity Tools- Generative AI tools for art and image creation; creative writing; (Dall- E3; Stable Diffusion(text-to-image model)
  • Generative and Discriminative Models integration
  • Ethical considerations and Risks of Generative AI